def testDrumsRNNPipeline(self): note_sequence = magenta.common.testing_lib.parse_test_proto( music_pb2.NoteSequence, """ time_signatures: { numerator: 4 denominator: 4} tempos: { qpm: 120}""") magenta.music.testing_lib.add_track_to_sequence( note_sequence, 0, [(36, 100, 0.00, 2.0), (40, 55, 2.1, 5.0), (44, 80, 3.6, 5.0), (41, 45, 5.1, 8.0), (64, 100, 6.6, 10.0), (55, 120, 8.1, 11.0), (39, 110, 9.6, 9.7), (53, 99, 11.1, 14.1), (51, 40, 12.6, 13.0), (55, 100, 14.1, 15.0), (54, 90, 15.6, 17.0), (60, 100, 17.1, 18.0)], is_drum=True) quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4) drums_extractor = drum_pipelines.DrumsExtractor(min_bars=7, gap_bars=1.0) one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MultiDrumOneHotEncoding()) quantized = quantizer.transform(note_sequence)[0] drums = drums_extractor.transform(quantized)[0] one_hot = one_hot_encoding.encode(drums) expected_result = { 'training_drum_tracks': [one_hot], 'eval_drum_tracks': [] } pipeline_inst = drums_rnn_pipeline.get_pipeline(self.config, eval_ratio=0.0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
def testDrumsRNNPipeline(self): note_sequence = magenta.common.testing_lib.parse_test_proto( music_pb2.NoteSequence, """ time_signatures: { numerator: 4 denominator: 4} tempos: { qpm: 120}""") magenta.music.testing_lib.add_track_to_sequence( note_sequence, 0, [(36, 100, 0.00, 2.0), (40, 55, 2.1, 5.0), (44, 80, 3.6, 5.0), (41, 45, 5.1, 8.0), (64, 100, 6.6, 10.0), (55, 120, 8.1, 11.0), (39, 110, 9.6, 9.7), (53, 99, 11.1, 14.1), (51, 40, 12.6, 13.0), (55, 100, 14.1, 15.0), (54, 90, 15.6, 17.0), (60, 100, 17.1, 18.0)], is_drum=True) quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4) drums_extractor = drum_pipelines.DrumsExtractor(min_bars=7, gap_bars=1.0) one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MultiDrumOneHotEncoding()) quantized = quantizer.transform(note_sequence)[0] drums = drums_extractor.transform(quantized)[0] one_hot = one_hot_encoding.encode(drums) expected_result = {'training_drum_tracks': [one_hot], 'eval_drum_tracks': []} pipeline_inst = drums_rnn_pipeline.get_pipeline( self.config, eval_ratio=0.0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) config = drums_rnn_config_flags.config_from_flags() pipeline_instance = drums_rnn_pipeline.get_pipeline( config, FLAGS.eval_ratio) FLAGS.input = os.path.expanduser(FLAGS.input) FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type), FLAGS.output_dir)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) config = drums_rnn_config_flags.config_from_flags() pipeline_instance = drums_rnn_pipeline.get_pipeline( config, FLAGS.eval_ratio) FLAGS.input = os.path.expanduser(FLAGS.input) FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type), FLAGS.output_dir)